README.md

Implementation of ASNets

In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems. By mimicking the relational structure of planning problems, ASNets are able to adopt a weight-sharing scheme which allows the network to be applied to any problem from a given planning domain. This allows the cost of training the network to be amortised over all problems in that domain. Further, we propose a training method which balances exploration and supervised training on small problems to produce a policy which remains robust when evaluated on larger problems. In experiments, we show that ASNet's learning capability allows it to significantly outperform traditional non-learning planners in several challenging domains.

This repository is structured as follows:

deepfpg/ contains our implementation and experiment files. The main entry point is
deepfpg/fpg.py. Consult
deepfpg/README.md for
instructions on installing and running the code.

models/ contains the trained models for our AAAI'18 paper. You can use those
models by passing the appropriate command line flags to
deepfpg/fpg.py.
For instance,
-m actprop -O num_layers=2,hidden_size=16 --resume-from models/ttw-adm/snapshot_31_1.000000.pkl
will load one of the two-layer networks which we trained for Triangle Tireworld.

problems/ includes all problems that we used to train + test the network, plus some problems
which might be helpful for further research or debugging.

If you use this code in an academic publication, we'd appreciate it if you cited the following paper: